1. Field of the Invention
The present invention relates generally to positioning and motion measurement. More particularly the invention relates to a navigation apparatus according to the preamble of claim 1 and a method for generating at least one navigation parameter according to claim 15.
2. Description of Related Art
In the last decades, navigation systems have been developed which facilitate the determination of a position dramatically compared to the earlier known methods. These navigation systems determine positions on basis of accurate radio signals received from a plurality of satellites (at least four), and are commonly referred to as global navigation satellite systems (GNSS:s). There exist several GNSS standards of which the U.S. GPS (Global Positioning System), the Russian correspondence GLONASS (Global Navigation Satellite System) and the European Galileo satellite system represent the most important examples.
Today, most GNSS receivers are primarily implemented in hardware, and are designed to operate exclusively in one of the above systems. However, various software solutions are also known for processing GNSS signals. For instance, the patent document WO02/50561 describes one example of a GPS receiver tracking system for guiding missiles.
If an even more accurate and reliable positioning is desired than what is possible to obtain by means of a purely GNSS-based system, data from a so-called Inertial Navigation System (INS) may also be used.
Solutions for integrating a GPS receiver with an INS have been studied in the literature. Namely, GPS and INS complement each other quite well due to their fundamental differences.
GPS receivers and INS:s measure different quantities. Accelerometers, found in inertial sensors, measure specific forces which are translated to a known coordinate frame with the aid of rate gyro derived measurements. Newton's laws of motion are then applied to provide velocity and position information. GPS is a radio navigation system. As such, GPS receivers measure satellites-to-user ranges, and from these measurements, the user position and velocity can be derived. Furthermore, GPS measurements have a long-term stability, however a noisy behavior. INS measurements on the other hand, are only short term stable; but are less noisy than GPS receivers. Moreover, GPS being a radio navigation system, is sensitive to external radio frequency disturbances, while an INS is not, since it is an autonomous system. Hence, an integration of GPS and INS promises to combine the best of both worlds.
A data collection unit presented in the article J. Thor and D. M. Akos, “A Direct RF Sampling Multifrequency GPS Receiver”, Proceedings of IEEE International Conference on Position Location and Navigation Symposium, Palm Springs, Calif., April 2002, pp. 44–51, and a GPS software radio presented in the article J. Thor, P-L. Normark, C Ståhlberg, “A High-Performance Real-Time GNSS Software Receiver and its Role in Evaluating Various Commercial Front End ASICs”, Proceedings of ION GPS 2002, Portland, Oreg., Sep. 24–27, 2002, pp. 2554–2560 constitute important components in a desired GPS/INS integration platform.
Both GPS and INS are widely used navigation systems. As mentioned above, the systems are different in nature and have their respective strengths and weaknesses. The characteristics of GPS and INS will now be briefly covered. After that follows a brief comparison of the both systems.
GPS is a satellite-based system, which provides an independent position and velocity solution at a rate in the order of 0.1–25 Hz. The precision of the GPS position solution outperforms any other radio navigation positioning system, especially when the price/performance ratios of the different systems are considered. The major strengths of GPS are the high accuracy and the low cost. The errors in the GPS position solution are bounded, however they contain constant or slowly varying biases. These biases may, for instance, be caused by radio signal delays in the troposphere and ionosphere, errors in the GPS emphemeris data and poor satellite geometry. Apart from the bounded biases, the GPS measurements are noisy. The specified received GPS signal power level is at −130 dBm. Thus, there is a visible thermal noise component. Other factors which contribute to the noisy behavior of GPS measurements are local oscillator imperfections and multipath errors. One technique to limit the effects of thermal noise in a GPS receiver is to design the code and carrier tracking loops to have as narrow bandwidth as possible. However a trade-off has to be made between noise performance and dynamics. When the bandwidths of the tracking loops are narrowed a more precise tracking is obtained, due to less noise being present in the pseudorange measurements. However, in a high dynamic environment the tracking loops may loose synchronization because the lock threshold is reduced. Moreover, being a radio navigation system, GPS relies on the availability of the satellite signals. If the line of sight vector from the user to the satellite is covered by obstacles the satellite signal may be lost. The radio signals may also be jammed (intentionally or unintentionally) or spoofed.
INS is an autonomous navigation system, which can provide independent measurements typically in the order of 10–500 Hz. A simplified block diagram of a known strapdown INS is shown in FIG. 1. By strapdown INS is here meant that an IMU 10, which contains the actual sensors, typically accelerometers 11 and gyros 12, is directly strapped down with a carrier and moves with the carrier. The output signals of the strapdown IMU 10 are angular rate Δθ and acceleration Δθ of the carrier.
A set of six sensors, three accelerometers and three gyros, sense the acceleration Δv and the angular rate Δθ of a body frame, and deliver this data to a navigation processor 20. The angular rate Δθ is corrected in an angular rate compensation unit 21 to produce a corrected angular rate ΔθC. This measure ΔθC is then integrated in a first integration unit 23 to obtain a measure of the body's attitude θ relative to the navigation frame. Correspondingly, any errors in the sensed acceleration Δv caused by known sensor imperfections are compensated for in an acceleration compensation unit 22, such that a corrected acceleration ΔvC is produced. A coordinate transform unit 24 receives the corrected acceleration ΔvC measure, and in response thereto generates a converted acceleration value ΔvCF with respect to the navigation frame. This measure ΔvCF is further corrected with a gravity model G, which is realized by means of a unit 25 and an adder 26, to produce a resulting acceleration ΔvCFG. Next, the resulting acceleration ΔvCFG is integrated once in a second integration unit 27 to obtain a velocity V and a second time in a third integration unit 28 to obtain position P. Each of the integration units 23, 27 and 28 are initialized with a respective appropriate start value θi, Vi, and Pi to render generation of correct navigation data possible.
There exist another types of IMU:s, such as gimbaled IMU:s. The gimbaled IMU consists of the actual sensors, accelerometers and gyros, which are mounted on a stabilized platform, so that the sensors are isolated from the rotations of the carrier. Naturally, the present invention can be used together with such systems as well.
Due to imperfections in the sensors, the measurements from accelerometers and rate gyros must normally be compensated. The sensors are calibrated, such that the measurements can be compensated for errors in the form of, for instance bias offsets and scale factor errors. The instrument compensation may derive its correction values from a priori known sensors errors or from an initial calibration procedure. However, certain types of errors cannot be handled by instrument compensation. These errors are, for instance, caused by white additive noise and slowly varying random drift of sensor biases. The noise term will result in a random walk after integration with an error variance which increases with time. The sensor bias errors will cause errors in position, velocity and attitude solutions which are unbounded. The integrations in the navigation processing further accumulate the bias. Hence, inertial sensors are not long term stable. On the other hand, the short-term error, i.e. the noise, is low. This can also be seen as an effect from the integrations in the navigation processing. Even if the sensor outputs are noisy the effect thereof will be smoothed by the integration process. Before navigation is started the INS must enter an initialization mode. Here, the sensor may be calibrated to compensate for random bias offsets in start-up. The initial state of the attitude must be determined in an alignment procedure. The initial velocity and position must also be known, so that the integrators can be provided with initial values.
Inertial sensors for precision navigation are very expensive, but with the current evolution of Micro Electrical Mechanical Systems (MEMS) inertial sensors the cost has dropped and is expected to continue to do so. At this date MEMS inertial sensors are by no means as good as state of the art non-MEMS inertial sensors. However MEMS inertial sensor quality is expected to improve.
A summary of different characteristics of GPS and INS is shown in table 1.
TABLE 1GPS and INS characteristicsAttributeGPSQuality INSCostRelatively cheapExpensiveType of systemRelies on external radio signalsAutonomousUpdate rateRelatively lowRelatively highBias errorsBoundedUnboundedNoiseRelatively highRelatively low
It is obvious that GPS and INS have different characteristics. The choice of which system to use for an application is totally dependent on the requirements of that application. However, if both systems are combined in an integrated system, large gains can be obtained in terms of performance and reliability.
Three different levels of GPS/INS integration are theoretically conceivable, namely representing a loose, a tight or an ultra-tight coupling the GPS receiver and the INS.
According to the loosely coupled approach, the GPS receiver and the INS independently compute position and velocity values. The computed values are then blended in a Kalman filter. The GPS derived position and velocity may be used to initialize and aid the INS in either a closed or an open loop configuration. By a closed loop configuration is here meant that feedback is provided from the GPS receiver to the INS; thereby continuously correcting for a drift in one or more IMU:s. This correction is preformed in order to reduce the position, velocity and attitude errors which originate from the IMU-sensor drift in time. In an open-loop configuration, there is no feedback loop. However, the INS relative position is reset from time to time (for example every 10:th second) with an absolute GPS derived position.
In a tightly coupled GPS/INS level of integration, GPS raw observables, pseudoranges and delta ranges are blended with inertial sensor measurements in a Kalman filter. The update rate of the Kalman filter is higher than in the loosely coupled configuration. Usually, the GPS position is updated every second. It is also necessary to pay more attention to the synchronization of the GPS and INS than in the loosely coupled configuration. The tightly coupled GPS/INS mode has one advantage over loosely ditto in that GPS errors may be modeled accurately. Thus, an optimal Kalman filter can be formulated. In similarity with a loosely coupled system, the GPS data can aid the INS system in a closed loop configuration.
However, it is currently relatively expensive to integrate a GPS receiver with an INS, for instance according to the above-described configurations. This is primarily due to the cost of the required high-quality IMU:s. Even though the cost of inertial sensors is expected to decrease significantly with the development of MEMS components a main obstacle remains for developing the GPS/INS algorithm area. There is namely yet no flexible platform for collecting the relevant data. The observability of GPS measurements is often rather limited in hardware GPS receivers. It is therefore by no means a simple task to interface with off-the-shelf components, such as hardware GPS receiver and an IMU.
Moreover, in order to integrate a GPS receiver with an INS it is necessary to consider synchronization of the measurements from the GPS receiver and the INS respectively. The GPS receiver and the INS namely have completely different sampling rates and are mutually unsynchronized. It is therefore inherently difficult to integrate a GNSS receiver (e.g. a GPS receiver) and an INS. The data-blending algorithm requires that the measurements from the GNSS and the INS be synchronized, i.e. that they are measured at the same time. The GPS is time-absolute and has a time stamp associated with every measurement. The INS system has no such timing markers. However, in the above-mentioned coupled systems, the INS measurements are usually time stamped by means of a “software clock”, which in turn is based on the GPS measurements. As INS measurements are received by the host processor appropriate values are interpolated to match relevant GPS measurement in time. In order to avoid this interpolation, a timing signal could be sent from the GPS receiver module to the IMU/INS module, wherein the timing signal indicates to the IMU/INS system when to perform measurements. However, this is still a sub-optimal solution, since the synchronization of the GPS receiver and the INS the will be far from perfect.